Merge pull request #43 from INM-6/master

New modules added, some module names updated, visualize convolved rate time series and bug fix

.gitignore

@@ -23,3 +23,5 @@ figures/Schmidt2018_dyn/.ipynb_checkpoints/*
 multiarea_model/.ipynb_checkpoints/*
 
 multiarea_model/data_multiarea/.ipynb_checkpoints/*
+
+tests/.ipynb_checkpoints/*

figures/MAM2EBRAINS/M2E_compute_corrcoeff.py

@@ -0,0 +1,61 @@
+import json
+import numpy as np
+import os
+
+import correlation_toolbox.helper as ch
+from multiarea_model import MultiAreaModel
+import sys
+
+"""
+Compute correlation coefficients for a subsample
+of neurons for the entire network from raw spike files of a given simulation.
+"""
+
+def compute_corrcoeff(data_path, label):
+    load_path = os.path.join(data_path,
+                             label,
+                             'recordings')
+    save_path = os.path.join(data_path,
+                             label,
+                             'Analysis')
+
+    with open(os.path.join(data_path, label, 'custom_params_{}'.format(label)), 'r') as f:
+        sim_params = json.load(f)
+    T = sim_params['T']
+
+    tmin = 500.
+    subsample = 2000
+    resolution = 1.
+
+    """
+    Create MultiAreaModel instance to have access to data structures
+    """
+    M = MultiAreaModel({})
+
+    spike_data = {}
+    cc_dict = {}
+    for area in M.area_list:
+        cc_dict[area] = {}
+        LvR_list = []
+        N = []
+        for pop in M.structure[area]:
+            fp = '-'.join((label,
+                           'spikes',  # assumes that the default label for spike files was used
+                           area,
+                           pop))
+            fn = '{}/{}.npy'.format(load_path, fp)
+            # +1000 to ensure that we really have subsample non-silent neurons in the end
+            spikes = np.load(fn)
+            ids = np.unique(spikes[:, 0])
+            dat = ch.sort_gdf_by_id(spikes, idmin=ids[0], idmax=ids[0]+subsample+1000)
+            bins, hist = ch.instantaneous_spike_count(dat[1], resolution, tmin=tmin, tmax=T)
+            rates = ch.strip_binned_spiketrains(hist)[:subsample]
+            cc = np.corrcoef(rates)
+            cc = np.extract(1-np.eye(cc[0].size), cc)
+            cc[np.where(np.isnan(cc))] = 0.
+            cc_dict[area][pop] = np.mean(cc)
+
+    fn = os.path.join(save_path,
+                      'corrcoeff.json')
+    with open(fn, 'w') as f:
+        json.dump(cc_dict, f)

figures/MAM2EBRAINS/M2E_compute_rate_time_series.py

@@ -0,0 +1,132 @@
+import json
+import neo
+import numpy as np
+import os
+import quantities as pq
+
+from multiarea_model.analysis_helpers import pop_rate_time_series
+from elephant.statistics import instantaneous_rate
+from multiarea_model import MultiAreaModel
+import sys
+
+"""
+Compute time series of population-averaged spike rates for a given
+area from raw spike files of a given simulation.
+
+Implements three different methods:
+- binned spike histograms on all neurons ('full')
+- binned spike histograms on a subsample of 140 neurons ('subsample')
+- spike histograms convolved with a Gaussian kernel of optimal width
+  after Shimazaki et al. (2010)
+"""
+
+# assert(len(sys.argv) == 5)
+
+# data_path = sys.argv[1]
+# label = sys.argv[2]
+# area = sys.argv[3]
+# method = sys.argv[4]
+
+def compute_rate_time_series(M, data_path, label, area, method):
+    assert(method in ['subsample', 'full', 'auto_kernel'])
+    # subsample : subsample spike data to 140 neurons to match the Chu 2014 data
+    # full : use spikes of all neurons and compute spike histogram with bin size 1 ms
+    # auto_kernel : use spikes of all neurons and convolve with Gaussian
+    #               kernel of optimal width using the method of Shimazaki et al. (2012)
+    #               (see Method parts of the paper)
+
+    load_path = os.path.join(data_path,
+                             label,
+                             'recordings')
+    save_path = os.path.join(data_path,
+                             label,
+                             'Analysis',
+                             'rate_time_series_{}'.format(method))
+    try:
+        os.mkdir(save_path)
+    except FileExistsError:
+        pass
+
+    with open(os.path.join(data_path, label, 'custom_params_{}'.format(label)), 'r') as f:
+        sim_params = json.load(f)
+    # T = sim_params['T']
+    T = sim_params['sim_params']['t_sim']
+
+    """
+    Create MultiAreaModel instance to have access to data structures
+    """
+    # M = MultiAreaModel({})
+
+    time_series_list = []
+    N_list = []
+    for pop in M.structure[area]:
+        fp = '-'.join((label,
+                       'spikes',  # assumes that the default label for spike files was used
+                       area,
+                       pop))
+        fn = '{}/{}.npy'.format(load_path, fp)
+        # spike_data = np.load(fn)
+        spike_data = np.load(fn, allow_pickle=True)
+        spike_data = spike_data[np.logical_and(spike_data[:, 1] > 500.,
+                                               spike_data[:, 1] <= T)]
+        if method == 'subsample':
+            all_gid = np.unique(spike_data[:, 0])
+            N = int(np.round(140 * M.N[area][pop] / M.N[area]['total']))
+
+            i = 0
+            s = 0
+            gid_list = []
+            while s < N:
+                rate = spike_data[:, 1][spike_data[:, 0] == all_gid[i]].size / (1e-3 * (T - 500.))
+                if rate > 0.56:
+                    gid_list.append(all_gid[i])
+                    s += 1
+                i += 1
+            spike_data = spike_data[np.isin(spike_data[:, 0], gid_list)]
+            kernel = 'binned_subsample'
+        if method == 'full':
+            N = M.N[area][pop]  # Assumes that all neurons were recorded
+            kernel = 'binned'
+
+        if method in ['subsample', 'full']:
+            time_series = pop_rate_time_series(spike_data, N, 500., T,
+                                               resolution=1.)
+
+        if method == 'auto_kernel':
+            # To reduce the computational load, the time series is only computed until 10500. ms
+            T = 10500.
+            N = M.N[area][pop]  # Assumes that all neurons were recorded
+            st = neo.SpikeTrain(spike_data[:, 1] * pq.ms, t_stop=T*pq.ms)
+            time_series = instantaneous_rate(st, 1.*pq.ms, t_start=500.*pq.ms, t_stop=T*pq.ms)
+            time_series = np.array(time_series)[:, 0] / N
+
+            kernel = 'auto'
+
+        time_series_list.append(time_series)
+        N_list.append(N)
+
+        fp = '_'.join(('rate_time_series',
+                       method,
+                       area,
+                       pop))
+        np.save('{}/{}.npy'.format(save_path, fp), time_series)
+
+    time_series_list = np.array(time_series_list)
+    area_time_series = np.average(time_series_list, axis=0, weights=N_list)
+
+    fp = '_'.join(('rate_time_series',
+                   method,
+                   area))
+    np.save('{}/{}.npy'.format(save_path, fp), area_time_series)
+
+    par = {'areas': M.area_list,
+           'pops': 'complete',
+           'kernel': kernel,
+           'resolution': 1.,
+           't_min': 500.,
+           't_max': T}
+    fp = '_'.join(('rate_time_series',
+                   method,
+                   'Parameters.json'))
+    with open('{}/{}'.format(save_path, fp), 'w') as f:
+        json.dump(par, f)

figures/MAM2EBRAINS/M2E_LOAD_DATA.py → figures/MAM2EBRAINS/M2E_load_data.py

@@ -93,7 +93,7 @@ def load_and_create_data(M, A, raster_areas):
         - 'alpha_time_window' : time constant of the alpha function
         - 'rect_time_window' : width of the moving rectangular function
     """
-    A.create_rate_time_series()
+    # A.create_rate_time_series()
     # print("Computing rate time series done")
 
 
@@ -188,4 +188,5 @@ def load_and_create_data(M, A, raster_areas):
     # try:
     #     subprocess.run(['python3', './Schmidt2018_dyn/compute_bold_signal.py'])
     # except FileNotFoundError:
-    #     raise FileNotFoundError("Executing R failed. Did you install R?")
+    #     raise FileNotFoundError("Executing R failed. Did you install R?")
+

figures/MAM2EBRAINS/M2E_visualize_resting_state.py

@@ -19,7 +19,9 @@
 icolor = myred
 ecolor = myblue
 
-from M2E_LOAD_DATA import load_and_create_data
+from M2E_load_data import load_and_create_data
+# from M2E_compute_corrcoeff import compute_corrcoeff
+from M2E_compute_rate_time_series import compute_rate_time_series
 
 def set_boxplot_props(d):
     for i in range(len(d['boxes'])):
@@ -66,6 +68,17 @@ def plot_resting_state(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
     # load data
     load_and_create_data(M, A, raster_areas)
 
+    # compute correlation_coefficient
+    # compute_correcoeff(data_path, M.simulation.label)
+
+    # compute rate_time_series_full
+    for area in raster_areas:
+        compute_rate_time_series(M, data_path, M.simulation.label, area, 'full')
+
+    # compute rate_time_series_auto_kernel
+    for area in raster_areas:
+        compute_rate_time_series(M, data_path, M.simulation.label, area, 'auto_kernel')
+
     t_sim = M.simulation.params["t_sim"]
 
     """
@@ -75,7 +88,7 @@ def plot_resting_state(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
     nrows = 4
     ncols = 4
     # width = 7.0866
-    width = 12
+    width = 10
     panel_wh_ratio = 0.7 * (1. + np.sqrt(5)) / 2.  # golden ratio
 
     height = width / panel_wh_ratio * float(nrows) / ncols
@@ -115,6 +128,7 @@ def plot_resting_state(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
     for area in raster_areas:
         if area not in area_list:
             raise Exception("Error! Given raster areas are either not from complete_area_list, please input correct areas to diaply the raster plots.")
+
     areas = raster_areas
 
     labels = ['A', 'B', 'C']
@@ -188,8 +202,12 @@ def plot_resting_state(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
     # Create MultiAreaModel instance to have access to data structures
     # """
     # M = MultiAreaModel({})
-
-    # spike data
+
+    spike_data = A.spike_data
+    label_spikes = M.simulation.label
+    label = M.simulation.label
+
+    # # spike data
     # spike_data = {}
     # for area in areas:
     #     spike_data[area] = {}
@@ -199,9 +217,6 @@ def plot_resting_state(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
     #                                                      'recordings',
     #                                                      '{}-spikes-{}-{}.npy'.format(label_spikes,
     #                                                                                   area, pop)))
-    spike_data = A.spike_data
-    label_spikes = M.simulation.label
-    label = M.simulation.label
 
     # stationary firing rates
     fn = os.path.join(data_path, label, 'Analysis', 'pop_rates.json')
@@ -211,23 +226,23 @@ def plot_resting_state(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
     # time series of firing rates
     rate_time_series = {}
     for area in areas:
-        # fn = os.path.join(data_path, label,
-        #                   'Analysis',
-        #                   'rate_time_series_full',
-        #                   'rate_time_series_full_{}.npy'.format(area))
         fn = os.path.join(data_path, label,
                           'Analysis',
-                          'rate_time_series-{}.npy'.format(area))
+                          'rate_time_series_full',
+                          'rate_time_series_full_{}.npy'.format(area))
+        # fn = os.path.join(data_path, label,
+        #                   'Analysis',
+        #                   'rate_time_series-{}.npy'.format(area))
         rate_time_series[area] = np.load(fn)
 
-    # # time series of firing rates convolved with a kernel
-    # rate_time_series_auto_kernel = {}
-    # for area in areas:
-    #     fn = os.path.join(data_path, label,
-    #                       'Analysis',
-    #                       'rate_time_series_auto_kernel',
-    #                       'rate_time_series_auto_kernel_{}.npy'.format(area))
-    #     rate_time_series_auto_kernel[area] = np.load(fn)
+    # time series of firing rates convolved with a kernel
+    rate_time_series_auto_kernel = {}
+    for area in areas:
+        fn = os.path.join(data_path, label,
+                          'Analysis',
+                          'rate_time_series_auto_kernel',
+                          'rate_time_series_auto_kernel_{}.npy'.format(area))
+        rate_time_series_auto_kernel[area] = np.load(fn)
 
     # local variance revised (LvR)
     fn = os.path.join(data_path, label, 'Analysis', 'pop_LvR.json')
@@ -445,7 +460,9 @@ def plot_resting_state(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
             np.logical_and(time >= t_min, time < t_max))]
         ax[i].plot(time, binned_spikes, color=colors[0], label=area)
         # rate = rate_time_series_auto_kernel[area]
-        # ax[i].plot(time, rate, color=colors[2], label=area)
+        rate = rate_time_series_auto_kernel[area][np.where(
+            np.logical_and(time >= t_min, time < t_max))]
+        ax[i].plot(time, rate, color=colors[2], label=area)
         ax[i].set_xlim((t_min, t_max))
 
         ax[i].text(0.8, 0.7, area, transform=ax[i].transAxes)